Multimodal iCub Simulation in MuJoCo (Vision, Touch, Proprioception) #733
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Awesome work @2103simon! We may consider these steps for the near / mid terms:
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Including a YARP wrapper might allow the use of the iCub skin GUI instead of my custom implementation. Not sure if this would also work for the motor GUI. We might want to consider keeping it simple tho and potentially have YARP only optional to keep the entry barrier as low as possible. What do you think? |
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Hi @2103simon, Recently, I had the opportunity to generate a simulation model of iCub directly starting from the CAD data. Through our discussions, we realized that we had both observed the model “exploding” at simulation startup. After some investigation and backtracking, I was able to identify and resolve the underlying issue. As a result, we now have a simulation model that is directly derived from the CAD, without requiring meshes to be split to avoid collisions. It is worth noting that this model has not yet been validated against a physical iCub, which would certainly be an interesting. That said, I would be happy to help if needed! |
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Adding here the link to the repo with my original implementation. https://github.com/hsp-iit/rl-icub-dexterous-manipulation Besides the implementation of the MuJoCo simulator, in the repo you can find the code for training RL policies as described in the G-PAYN and RESPRECT papers. The code is meant to be YARP-free (robotogy-superbuild is installed to use the iKin IK solver, which turned out to be the best option for iCub), but it also provides an integration with YARP for training RL policies on the real robot. |
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Hello @everyone,
There has been great work bringing iCub into MuJoCo already. Building on the work of @fedeceola, I’ve extended the simulator to include full-body tactile sensing (taxels) and optional event-driven outputs:
https://github.com/event-driven-robotics/neuromorphic_body_schema
Key features:
The simulator is designed as a flexible platform for multimodal robotics, including tasks such as tactile interaction, reach-and-grasp, manipulation, and sensorimotor learning.
This is still work in progress. Known limitations include sim-to-real gaps (e.g., PID tuning, skin response magnitude, and cross-talk).
I’d really appreciate feedback from the community:
If there’s interest, I’m considering scaling up development effort in this direction.
Thanks!
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